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Glossary

A comprehensive list of technical terms and AI-specific terminology used throughout the "Physical AI & Humanoid Robotics" book.

Technical Jargon​

  • Actuator: A component of a machine that is responsible for moving or controlling a mechanism or system. It takes energy, usually created by air, electric current, or liquid, and converts it into some kind of motion.
  • Degrees of Freedom (DoF): The number of independent parameters that define the configuration of a mechanical system. In robotics, this often refers to the number of independent joint movements a robot possesses.
  • End-Effector: The device or tool at the end of a robotic arm, designed to interact with the environment. Examples include grippers, welding torches, or cameras.
  • Kinematics: The study of motion without considering the forces that cause it. In robotics, it describes the geometry of motion for a robot arm, including position, velocity, and acceleration.
  • SLAM (Simultaneous Localization and Mapping): A computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
  • Teleoperation: The operation of a machine or robot from a distance, often with human control providing input.

AI-Specific Terminology​

  • Artificial General Intelligence (AGI): Hypothetical AI that can understand, learn, and apply intelligence to any intellectual task that a human being can.
  • Computer Vision: A field of AI that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and take actions or make recommendations based on that information.
  • Deep Learning: A subset of machine learning in which artificial neural networks, inspired by the human brain, learn from large amounts of data.
  • Machine Learning (ML): A branch of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.
  • Neural Network: A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
  • Reinforcement Learning (RL): An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
  • Robotics Process Automation (RPA): Software technology that makes it easy to build, deploy, and manage software robots that emulate humans actions interacting with digital systems and software.
  • Transfer Learning: A machine learning method where a model developed for a task is reused as the starting point for a model on a second task.